Volumetric Analysis of Brain MRI for Alzheimer's Disease

144 Pages·2015·6.76 MB·English

University of Miami
Scholarly Repository
Open Access Dissertations Electronic Theses and Dissertations
2011-05-09
Volumetric Analysis of Brain MRI for Alzheimer’s
Disease
Qian Shen
University of Miami, [email protected]
Follow this and additional works at:https://scholarlyrepository.miami.edu/oa_dissertations
Recommended Citation
Shen, Qian, "Volumetric Analysis of Brain MRI for Alzheimer’s Disease" (2011).Open Access Dissertations. 526.
https://scholarlyrepository.miami.edu/oa_dissertations/526
This Open access is brought to you for free and open access by the Electronic Theses and Dissertations at Scholarly Repository. It has been accepted for
inclusion in Open Access Dissertations by an authorized administrator of Scholarly Repository. For more information, please contact
[email protected].

UNIVERSITY OF MIAMI

VOLUMETRIC ANALYSIS OF BRAIN MRI FOR ALZHEIMER‟S DISEASE

By

Qian Shen

A DISSERTATION

Submitted to the Faculty
of the University of Miami
in partial fulfillment of the requirements for
the degree of Doctor of Philosophy

Coral Gables, Florida

May 2011
©2011
Qian Shen
All Rights Reserved
UNIVERSITY OF MIAMI

A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy

VOLUMETRIC ANALYSIS OF BRAIN MRI FOR ALZHEIMER‟S DISEASE

Qian Shen

Approved:

___________________ ___________________
Weizhao Zhao, Ph.D. Terri A. Scandura, Ph.D.
Professor of Biomedical Engineering Dean of the Graduate School

___________________ ___________________
Özcan Özdamar, Ph.D. Jorge E. Bohórquez, Ph.D.
Professor of Biomedical Engineering Professor of Biomedical Engineering

___________________ ___________________
Ranjan Duara, M.D. Mohamed Abdel-Mottaleb, Ph.D.
Medical Director of Wien Center Professor of Electrical and Computer
Mount Sinai Medical Center Engineering
SHEN, QIAN (Ph.D., Biomedical Engineering)
Volumetric Analysis of Brain MRI for Alzheimer‟s Disease (May 2011)

Abstract of a dissertation at the University of Miami.

Dissertation supervised by Professor Weizhao Zhao.
No. of pages in text. (124)

Alzheimer‟s disease (AD), the most common cause of dementia in the elderly, is a
gradually progressive degenerative neurological disorder that is characterized by
increasing cognitive impairment, characteristic degenerative pathology and brain atrophy.
Studies have shown that the progression of AD pathology in the brain develops in a
predictable pattern and the pathological changes that take place in brain begin at the
microscopic level long before the first signs of memory loss. Structural Magnetic
Resonance Imaging (MRI), which has exceptional soft tissue contrast and detailed
resolution, is the best way to noninvasively examine changes which occur early in the
course of AD. For this dissertation, our aim is to improve the methods for measuring the
atrophy of brain structures in AD, as seen on MRI, and to apply these methods to subjects
with cognitive impairment.
This study has established a new coordinate template to replace the widely used Montreal
Neurological Institute (MNI) template for the atlas-based segmentation procedure. The
new template was derived from the same structural image as the one used by the
Automated Anatomical Labeling (AAL) procedure. The agreement of the newly
developed coordinate template and AAL helps to estimate accurate spatial transformation
parameters used in warping the AAL to individual subject images. The new template
combines the spatial information of the structural image and the frequency information of
MNI template. Based on the same principle, a set of customized templates has been
developed. The customized template, associated atlas and customized priors match more
closely the aging population than the previous template, so as to improve the atlas-based
segmentation of regions of interest in AD assessment.
Visual Rating System (VRS) of a single coronal slice (MB slice) in MRI has been
another valuable method in the assessment of medial temporal lobe atrophy. An
automated procedure has been developed in this study to measure the hippocampal area
on the same coronal slice so that the labor of human experts in the VRS assessment of
hippocampus will be significantly reduced.
Finally the methods and materials (template and atlas) developed in this dissertation were
applied to cross-sectional studies of subjects with cognitive impairment. We conducted
volumetric analysis on subjects and conclude that the data from the new approaches have
higher correlations with clinical data, and therefore can be reliably used as part of an AD
assessment tool.
ACKNOWLEDGMENT

I would like to express my deepest respect and gratitude to my advisor, Professor
Weizhao Zhao, for giving me the opportunity to start my Ph.D. program in his laboratory
and for his continued support, guidance, encouragement and patience throughout my
Ph.D. study at the University of Miami.
I also want to convey thanks to my internship supervisor and research collaborator, Dr.
Ranjan Duara, for allowing me to work at the Wien Center for Alzheimer‟s Disease and
Memory Disorders, Mount Sinai Medical Center, and from whom I have learned a lot
about Alzheimer‟s Disease and human brain anatomy. Without him this study would not
have been possible.
I am also grateful to Professor Jorge E. Bohorquez for teaching me LabView and for
cheering me on and inspiring me with his enthusiasm when I felt overwhelmed.
I also wish to thank Professor Özcan Özdamar and Professor Mohamed Abdel-Mottaleb
for their suggestions and comments during the preparation of this dissertation.
Thanks to my fellow Ph.D. students Ta-Cheng Chang, Tai Yi Yuan, and Decho
Surangsrirat for all the helpful discussions, fun times and activities. I have no doubt you
will succeed in all you undertake.
I would like to thank the members of the Wien Center for Alzheimer‟s Disease and
Memory Disorders at Mount Sinai Medical Center: Warren Barker, Elizabeth Potter,
Yirah Ochoa, Isael Santos, Maria Greig-Custo, Celeste Ramirez, Joscelyn Agron, and
Daisy Wendy Acevedo. You are all so nice to me and it has been a pleasure working with
all of you in a friendly atmosphere. I owe a tremendous debt of gratitude to Warren
iii
Barker for teaching me how to use statistical analysis tools and for doing the English
proof-reading of my dissertation.
Finally, I would like to extend my deepest grateful to my parents, my husband, Tao, and
my daughter, Qiqi, for their unconditional support. Without their constant love,
understanding and encouragement, it would have been impossible to finish this study.

iv
TABLE OF CONTENTS

LIST OF FIGURES ..................................................................................................... viii
LIST OF TABLES ....................................................................................................... xiii
LIST OF ABBREVIATIONS ...................................................................................... xiv
1 Introduction
1.1 Dementia, AD and MCI ............................................................................ 1
1.1.1 Neuropathology of AD ………………………………… ................... 3
1.1.2 Clinico - Pathological Correlations in AD …………………………. . 4
1.1.3 Clinical Diagnosis of AD …………………………………. ............... 5
1.1.4 Treatment of AD …………………………. ........................................ 9
1.1.5 Use of Biomarkers for Diagnosis of Prodromal (PreClinical) AD…. . 10
1.2 Structural MRI in Detection of Atrophy in AD ......................................... 13
1.2.1 Visual Rating Methods ........................................................................ 15
1.2.2 Volumetric Analysis ……………………………. .............................. 19
1.3 Problem Statement ..................................................................................... 28
1.4 Thesis Contributions .................................................................................. 30
2 Development of a New Coordinate Template
2.1 Image Registration ..................................................................................... 31
2.1.1 Affine Transform ………………………………………… ................ 31
2.1.2 Non-linear Registration ………………………………………… ....... 34
2.2 Image Segmentation of Different Tissue Classes ...................................... 37
v